Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Computerized tomography (CT) involves the imaging of the internal structure of a target object by collecting several projection images (“radiographic projections”) in a single scan operation (“scan”), and is widely used in the medical field to view the internal structure of selected portions of the human body. In an ideal imaging system, rays of radiation travel along respective straight-line transmission paths from the radiation source, through a target object, and then to respective pixel detectors of the imaging system without generating scattered rays. However, in real systems, when a quantum of radiation is absorbed by a portion of the target object, one or more scattered rays are often generated that deviate from the transmission path of the incident radiation. These scattered rays are often received by “surrounding” detector elements that are not located on the transmission path that the initial quantum of radiation was transmitted on, thereby creating measurement errors.
The measurement errors created by scattered radiation cause artifacts and loss of spatial and contrast resolution in the radiographic projection data and the CT images produced by the imaging system. The scattered radiation can also cause numerical errors in image reconstruction algorithms. All of the foregoing leads to image degradation.
Solutions have been proposed to estimate and/or correct scattered radiation using kernel methods. In one example solution, U.S. patent application Ser. No. 12/125,053 discloses symmetric and asymmetric kernel models, which is hereby incorporated by reference in its entirety. In other example solutions, U.S. Pat. No. 8,199,873, issued on Jun. 12, 2012, and U.S. patent application Ser. No. 13/485,953, filed on Jun. 1, 2012, discloses hybrid kernel models. Due to the limitations in the scatter estimation models employed in these solutions, approximately +/−50 Hounsfield Units (HUs) uncertainties still exist for challenging situations such as pelvis scans.
Accordingly, there is a need to develop techniques that can further improve the estimation accuracy but in an efficient manner.